Fusion

Abstract: In this paper, we present a novel deep learning architecture for infrared and visible images fusion problems. In contrast to conventional convolutional networks, our encoding network is combined with convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in the encoding process, and two fusion layers (fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by a decoder. Compared with existing fusion methods, the proposed fusion method achieves the state-of-the-art performance in objective and subjective assessment.

Abstract: We present a review of experimental, theoretical, and molecular simulation studies of confinement effects on freezing and melting We consider both simple and more complex adsorbates that are confined in various environments (slit or cylindrical pores and also disordered porous materials) The most commonly used molecular simulation, theoretical and experimental methods are first presented We also provide a brief description of the most widely used porous materials The current state of knowledge on the effects of confinement on structure and freezing temperature, and the appearance of new surface-driven and confinement-driven phases are then discussed We also address how confinement affects the glass transition